





"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	(UnCLIPScheduler,)







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									__a			:    List[Any]      							=	{
									    '''num_train_timesteps''': 1000,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**_UpperCAmelCase  )
									return config







					def 			_lowerCamelCase		(    self  ):
									for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for variance in ["fixed_small_log", "learned_range"]:
													self.check_over_configs(variance_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for clip_sample_range in [1, 5, 10, 20]:
													self.check_over_configs(clip_sample_range=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(prediction_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for time_step in [0, 500, 999]:
													for prev_timestep in [None, 5, 100, 250, 500, 750]:
																	if prev_timestep is not None and prev_timestep >= time_step:
																					continue

																	self.check_over_forward(time_step=_UpperCAmelCase						,      prev_timestep=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.scheduler_classes[0]
									__a			:    Any      							=	self.get_scheduler_config(variance_type='''fixed_small_log'''  )
									__a			:    Optional[Any]      							=	scheduler_class(**_UpperCAmelCase  )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0_0_0_0e-1_0  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_5_4_9_6_2_5  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.9_9_9_4_9_8_7  )  ) < 1e-5







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	self.scheduler_classes[0]
									__a			:    Any      							=	self.get_scheduler_config(variance_type='''learned_range'''  )
									__a			:    Optional[int]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    int      							=	0.5

									assert scheduler._get_variance(1						,      predicted_variance=_UpperCAmelCase  ) - -1_0.1_7_1_2_7_9_0 < 1e-5
									assert scheduler._get_variance(487						,      predicted_variance=_UpperCAmelCase  ) - -5.7_9_9_8_0_5_2 < 1e-5
									assert scheduler._get_variance(999						,      predicted_variance=_UpperCAmelCase  ) - -0.0_0_1_0_0_1_1 < 1e-5







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	self.scheduler_classes[0]
									__a			:    Dict      							=	self.get_scheduler_config()
									__a			:    List[str]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Dict      							=	scheduler.timesteps

									__a			:    Optional[int]      							=	self.dummy_model()
									__a			:    Any      							=	self.dummy_sample_deter
									__a			:    Optional[int]      							=	torch.manual_seed(0  )

									for i, t in enumerate(_UpperCAmelCase  ):
													# 1. predict noise residual
													__a			:    List[Any]      							=	model(_UpperCAmelCase						,      _UpperCAmelCase  )

													# 2. predict previous mean of sample x_t-1
													__a			:    Dict      							=	scheduler.step(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      generator=_UpperCAmelCase  ).prev_sample

													__a			:    Tuple      							=	pred_prev_sample

									__a			:    Optional[Any]      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    Any      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5  ) < 1e-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.scheduler_classes[0]
									__a			:    str      							=	self.get_scheduler_config()
									__a			:    Union[str, Any]      							=	scheduler_class(**_UpperCAmelCase  )

									scheduler.set_timesteps(25  )

									__a			:    Tuple      							=	scheduler.timesteps

									__a			:    Any      							=	self.dummy_model()
									__a			:    Optional[Any]      							=	self.dummy_sample_deter
									__a			:    Optional[int]      							=	torch.manual_seed(0  )

									for i, t in enumerate(_UpperCAmelCase  ):
													# 1. predict noise residual
													__a			:    List[Any]      							=	model(_UpperCAmelCase						,      _UpperCAmelCase  )

													if i + 1 == timesteps.shape[0]:
																	__a			:    List[Any]      							=	None
													else:
																	__a			:    str      							=	timesteps[i + 1]

													# 2. predict previous mean of sample x_t-1
													__a			:    List[str]      							=	scheduler.step(
													    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      prev_timestep=_UpperCAmelCase						,      generator=_UpperCAmelCase  ).prev_sample

													__a			:    Tuple      							=	pred_prev_sample

									__a			:    Any      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    Dict      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3  ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									pass







					def 			_lowerCamelCase		(    self  ):
									pass







'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								# For consistency across different places the DisjunctiveConstraint is called,
								# dc.token_ids is a list of integers. It is also initialized only by integers.

								UpperCAmelCase_      :       Union[str, Any]          =       [[1, 2, 4], [1, 2, 3, 4]]
								UpperCAmelCase_      :       Dict          =       DisjunctiveConstraint(_A      )
								self.assertTrue(isinstance(dc.token_ids				, _A      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								# We can't have constraints that are complete subsets of another. This leads to a preverse
								# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
								# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
								# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
								# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
								UpperCAmelCase_      :       Optional[Any]          =       [[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(_A      )  # fails here




					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       [[1, 2, 3], [1, 2, 4]]

								UpperCAmelCase_      :       List[Any]          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								UpperCAmelCase_      :       int          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(2      )
								UpperCAmelCase_      :       Optional[int]          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(3      )
								UpperCAmelCase_      :       List[Any]          =       stepped is True and completed is True and reset is False
								self.assertTrue(_A      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]      )





					def 		A      (  self  :		List[Any]      )    ->							Any:
								UpperCAmelCase_      :       int          =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								UpperCAmelCase_      :       str          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(4      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2, 4]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

								dc.reset()

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 3      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 2      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.remaining() == 0      )
								self.assertTrue(dc.current_seq == [1, 2, 5]      )


